Collagen-Derived Cryptides: Machine-Learning Prediction and Molecular Dynamic Interaction Against Klebsiella pneumoniae Biofilm Synthesis Precursor
Keywords:
Collagen, antibiofilm peptide (AMP), Klebsiella pneumoniae, molecular docking, MrkH, Type 3 fimbriaeAbstract
Collagen-derived cryptic peptides (cryptides) are biologically active peptides derived from the proteolytic digestion of collagen protein. These cryptides possess a multitude of activities, including antihypertensive, antiproliferative, and antibacterial. The latter, however, has not been extensively studied. The cryptides are mainly obtained from the protein hydrolysate, followed by characterizations to elucidate the function, limiting the number of cryptides investigated within a short period. The recent threat of antimicrobial resistance microorganisms (AMR) to global health requires the rapid development of new therapeutic drugs. The current study aims to predict antimicrobial peptides (AMP) from collagen-derived cryptides, followed by elucidating their potential to inhibit biofilm-related precursors in Klebsiella pneumoniae using in silico approach. Therefore, cryptides derived from collagen amino acid sequences of various types and species were subjected to online machine-learning platforms (i.e., CAMPr3, DBAASP, dPABBs, Hemopred, and ToxinPred). The peptide-protein interaction was elucidated using molecular docking, molecular dynamics, and MM-PBSA analysis against MrkH, a K. pneumoniae’s transcriptional regulator of type 3 fimbriae that promote biofilm formation. As a result, six potential antibiofilm inhibitory cryptides were screened and docked against MrkH. All six peptides bind stronger than the MrkH ligand (c-di-GMP; C2E).
Downloads
Metrics
References
Abdillahi, H.S., Verschaeve, L., Finnie, J.F. & Van Staden, J. 2012. Mutagenicity, antimutagenicity and cytotoxicity evaluation of South African Podocarpus species. Journal of Ethnopharmacology, 139(3): 728-738. DOI: https://doi.org/10.1016/j.jep.2011.11.044
Abdillahi, S. M., Bober, M., Nordin, S., Hallgren, O., Baumgarten, M., Erjefält, J., Westergren-Thorsson, G., Bjermer, L., Riesbeck, K., Egesten, A., & Mörgelin, M. 2015. Collagen VI is upregulated in COPD and serves both as an adhesive target and a bactericidal barrier for Moraxella catarrhalis. Journal of Innate Immunity, 7(5): 506-517. DOI: https://doi.org/10.1159/000381213
Abdillahi, S. M., Maaß, T., Kasetty, G., Strömstedt, A. A., Baumgarten, M., Tati, R., Nordin, S. L., Walse, B., Wagener, R., Schmidtchen, A., & Mörgelin, M. 2018. Collagen VI contains multiple host defense peptides with potent in vivo activity. The Journal of Immunology, 201(3): 1007-1020. DOI: https://doi.org/10.4049/jimmunol.1700602
Adekoya, O. A., & Sylte, I. 2009. The Thermolysin Family (M4) of Enzymes: Therapeutic and biotechnological potential. Chemical Biology & Drug Design, 73(1): 7-16. DOI: https://doi.org/10.1111/j.1747-0285.2008.00757.x
Atef, M., Chait, Y.A., Ojagh, S.M., Latifi, A.M., Esmaeili, M., Hammami, R. & Udenigwe, C.C. 2021. Anti-Salmonella activity and peptidomic profiling of peptide fractions produced from sturgeon fish skin collagen (Huso huso) using commercial enzymes. Nutrients, 13(8): 2657. DOI: https://doi.org/10.3390/nu13082657
Baehaki, A., Suhartono, M. T., Sukarno, Syah, D., & Setyahadi, S. 2016. Collagen peptides from fish skin with Angiotensin I-Converting Enzyme (ACE) inhibitor and cancer antiproliferative activity. Research Journal of Pharmaceutical, Biological and Chemical Sciences, 7(1): 1994-2000.
Bahar, A.A. & Ren, D. 2013. Antimicrobial peptides. Pharmaceuticals, 6(12): 1543-1575. DOI: https://doi.org/10.3390/ph6121543
Banerjee, P., & Shanthi, C. 2016. Cryptic Peptides from Collagen: A Critical Review. Protein and Peptide Letters, 23(7): 664-672. DOI: https://doi.org/10.2174/0929866522666160512151313
Berendsen, H.J.C., Postma, J.P.M., van Gunsteren, W.F. DiNola, A. & Haak, J.R. 1984. Molecular dynamics with coupling to an external bath. The Journal of Chemical Physics, 81(8): 3684-3690. DOI: https://doi.org/10.1063/1.448118
Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N. & Bourne, P.E. 2000. The Protein Data Bank. Nucleic Acids Research, 28(1): 235-242. DOI: https://doi.org/10.1093/nar/28.1.235
Blatt, J. M. & Weisskopf, V.F. 1979. Theoretical nuclear physics. In: Theoretical Nuclear Physics (1st Ed.). Springer New York. DOI: https://doi.org/10.1007/978-1-4612-9959-2_1
Bradford, A., Raftery, M., Bowie, J., Tyler, M., Wallace, J., Adams, G. & Severini, C. 1996. Novel uperin peptides from the dorsal glands of the Australian floodplain toadlet Uperoleia inundata. Australian Journal of Chemistry, 49(4): 475-484. DOI: https://doi.org/10.1071/CH9960475
Budagavi, D.P. & Chugh, A. 2018. Antibacterial properties of Latarcin 1 derived cell-penetrating peptides. European Journal of Pharmaceutical Sciences, 115: 43-49. DOI: https://doi.org/10.1016/j.ejps.2018.01.015
Chen, Y., Guarnieri, M.T., Vasil, A.I., Vasil, M.L., Mant, C.T. & Hodges, R.S. 2007. Role of peptide hydrophobicity in the mechanism of action of alpha-helical antimicrobial peptides. Antimicrobial Agents and Chemotherapy, 51(4): 1398-1406. DOI: https://doi.org/10.1128/AAC.00925-06
Chung, C.-R., Jhong, J.-H., Wang, Z., Chen, S., Wan, Y., Horng, J.-T. & Lee, T.-Y. 2020. Characterization and identification of natural antimicrobial peptides on different organisms. International Journal of Molecular Sciences, 21(3): 986. DOI: https://doi.org/10.3390/ijms21030986
Conlon, J.M., Sonnevend, A., Davidson, C., Smith, D.D. & Nielsen, P.F. 2004. The ascaphins: A family of antimicrobial peptides from the skin secretions of the most primitive extant frog, Ascaphus truei. Biochemical and Biophysical Research Communications, 320(1): 170-175. DOI: https://doi.org/10.1016/j.bbrc.2004.05.141
Dobson, A., Cotter, P.D., Ross, R.P. & Hill, C. 2012. Bacteriocin production: A probiotic trait? Applied and Environmental Microbiology, 78(1): 1-6. DOI: https://doi.org/10.1128/AEM.05576-11
Dressler, P., Gehring, D., Zdzieblik, D., Oesser, S., Gollhofer, A. & König, D. 2018. Improvement of functional ankle properties following supplementation with specific collagen peptides in athletes with chronic ankle instability. Journal of Bodywork and Movement Therapies, 22(4): 858. DOI: https://doi.org/10.1016/j.jbmt.2018.09.037
Elber, R., Ruymgaart, A.P. & Hess, B. 2011. SHAKE parallelization. The European Physical Journal, 200(1): 211-223. DOI: https://doi.org/10.1140/epjst/e2011-01525-9
Ennaas, N., Hammami, R., Gomaa, A., Bédard, F., Biron, É., Subirade, M., Beaulieu, L., & Fliss, I. 2016. Collagencin, an antibacterial peptide from fish collagen: Activity, structure and interaction dynamics with membrane. Biochemical and Biophysical Research Communications, 473(2): 642-647. DOI: https://doi.org/10.1016/j.bbrc.2016.03.121
Gasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S., Wilkins, M.R., Appel, R.D. & Bairoch, A. 2005. Protein Identification and Analysis Tools on the ExPASy Server BT. In: The Proteomics Protocols Handbook. J.M. Walker (Ed.). Humana Press. pp. 571-607. https://doi.org/10.1385/1-59259-890-0:571 DOI: https://doi.org/10.1385/1-59259-890-0:571
Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., Open Source Drug Discovery Consortium & Raghava, G.P.S. 2013. In silico approach for predicting toxicity of peptides and proteins. PLoS One, 8(9): e73957. DOI: https://doi.org/10.1371/journal.pone.0073957
He, J., Luo, X., Jin, D., Wang, Y., & Zhang, T. 2018. Identification, recombinant expression, and characterization of LHG2, a novel antimicrobial peptide of Lactobacillus casei HZ1. Molecules, 23(9): 2246. DOI: https://doi.org/10.3390/molecules23092246
Holton, T.A., Pollastri, G., Shields, D.C. & Mooney, C. 2013. CPPpred: prediction of cell penetrating peptides. Bioinformatics, 29(23): 3094-3096. DOI: https://doi.org/10.1093/bioinformatics/btt518
Honorato, R.V., Koukos, P.I., Jiménez-García, B., Tsaregorodtsev, A., Verlato, M., Giachetti, A., Rosato, A. & Bonvin, A.M.J.J. 2021. Structural Biology in the Clouds: The WeNMR-EOSC Ecosystem. Frontiers in Molecular Biosciences, 8: 729513. DOI: https://doi.org/10.3389/fmolb.2021.729513
Houri, A.J. & Mechler, A. 2020. Mechanism of action of the antimicrobial peptide Caerin1.1. ChemistrySelect, 5(20): 5895-5902. DOI: https://doi.org/10.1002/slct.202000851
Hubbard, R.E. & Kamran Haider, M. 2010. Hydrogen Bonds in Proteins: Role and Strength. In eLS. Wiley. DOI: https://doi.org/10.1002/9780470015902.a0003011.pub2
Izaguirre, J.A., Catarello, D.P., Wozniak, J.M. & Skeel, R.D. 2001. Langevin stabilization of molecular dynamics. The Journal of Chemical Physics, 114(5): 2090-2098. DOI: https://doi.org/10.1063/1.1332996
Jiang, Z., Vasil, A.I., Hale, J.D., Hancock, R.E.W., Vasil, M.L. & Hodges, R.S. 2008. Effects of net charge and the number of positively charged residues on the biological activity of amphipathic alpha-helical cationic antimicrobial peptides. Biopolymers, 90(3): 369-383. DOI: https://doi.org/10.1002/bip.20911
Korkmaz, B., Horwitz, M.S., Jenne, D.E. & Gauthier, F. 2010. Neutrophil elastase, proteinase 3, and cathepsin G as therapeutic targets in human diseases. Pharmacological Reviews, 62(4): 726-759. DOI: https://doi.org/10.1124/pr.110.002733
Krieger, E. & Vriend, G. 2015. New ways to boost molecular dynamics simulations. Journal of Computational Chemistry, 36(13): 996-1007. DOI: https://doi.org/10.1002/jcc.23899
Krieger, E., Koraimann, G. & Vriend, G. 2002. Increasing the precision of comparative models with YASARA NOVA--a self-parameterizing force field. Proteins, 47(3): 393-402. DOI: https://doi.org/10.1002/prot.10104
Krieger, E., Nielsen, J.E., Spronk, C.A.E.M. & Vriend, G. 2006. Fast empirical pKa prediction by Ewald summation. Journal of Molecular Graphics & Modelling, 25(4): 481-486. DOI: https://doi.org/10.1016/j.jmgm.2006.02.009
Lamiable, A., Thévenet, P., Rey, J., Vavrusa, M., Derreumaux, P. & Tufféry, P. 2016. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Research, 44(W1): W449-54. DOI: https://doi.org/10.1093/nar/gkw329
Land, H. & Humble, M.S. 2018. YASARA: A tool to obtain structural guidance in biocatalytic investigations. Methods in Molecular Biology, 1685: 43-67. DOI: https://doi.org/10.1007/978-1-4939-7366-8_4
Lata, S., Sharma, B. & Raghava, G. 2007. Analysis and prediction of antibacterial peptides. BMC Bioinformatics, 8(1): 263. DOI: https://doi.org/10.1186/1471-2105-8-263
Mahlapuu, M., Håkansson, J., Ringstad, L. & Björn, C. 2016. Antimicrobial Peptides: An emerging category of therapeutic agents. Frontiers in Cellular and Infection Microbiology, 6: 194. DOI: https://doi.org/10.3389/fcimb.2016.00194
Maier, J.A., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K.E. & Simmerling, C. 2015. ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. Journal of Chemical Theory and Computation, 11(8): 3696-3713. DOI: https://doi.org/10.1021/acs.jctc.5b00255
Mao, Y., Niu, S., Xu, X., Wang, J., Su, Y., Wu, Y. & Zhong, S. 2013. The effect of an adding histidine on biological activity and stability of pc-pis from Pseudosciaena crocea. PLoS ONE, 8(12): e83268. DOI: https://doi.org/10.1371/journal.pone.0083268
Martins, P.M., Santos, L.H., Mariano, D., Queiroz, F.C., Bastos, L.L., Gomes, I. de S., Fischer, P.H. C., Rocha, R.E.O., Silveira, S. A., de Lima, L.H.F., de Magalhães, M.T.Q., Oliveira, M.G.A. & de Melo-Minardi, R. C. 2021. Propedia: A database for protein-peptide identification based on a hybrid clustering algorithm. BMC Bioinformatics, 22(1): 1. DOI: https://doi.org/10.1186/s12859-020-03881-z
Mobarki, N., Almerabi, B. & Hattan, A. 2019. Antibiotic resistance crisis. International Journal of Medicine in Developing Countries, 3(6): 561-564. DOI: https://doi.org/10.24911/IJMDC.51-1549060699
Murray, C.J., Ikuta, K.S., Sharara, F., Swetschinski, L., Robles Aguilar, G., Gray, A., Han, C., Bisignano, C., Rao, P., Wool, E., Johnson, S.C., Browne, A.J., Chipeta, M.G., Fell, F., Hackett, S., Haines-Woodhouse, G., Kashef Hamadani, B.H., Kumaran, E.A.P., McManigal, B., … Naghavi, M. 2022. Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. The Lancet, 399(10325): 629-655. DOI: https://doi.org/10.1016/S0140-6736(21)02724-0
O’Neill, J. 2016. Tackling drug-resistant infections globally: Final report and recommendations. London: HM Government and Wellcome Trust. Review on Antimicrobial Resistance, chaired by Jim O’Neill. https://amr-review.org/sites/default/files/160518_Final%20paper_with%20cover.pdf
Osorio, D., Rondón-Villarreal, P. & Torres, R. 2015. Peptides: A package for data mining of antimicrobial peptides. The R Journal, 7(1): 4. DOI: https://doi.org/10.32614/RJ-2015-001
Pan, Y.A., Xiao, X. & Wang, P. 2012. AMPpred: An on-line predictor design for automated AMP recognition. Applied Mechanics and Materials, 229-231: 2276-2279. DOI: https://doi.org/10.4028/www.scientific.net/AMM.229-231.2276
Pirtskhalava, M., Amstrong, A.A., Grigolava, M., Chubinidze, M., Alimbarashvili, E., Vishnepolsky, B., Gabrielian, A., Rosenthal, A., Hurt, D.E. & Tartakovsky, M. 2021. DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Research, 49(D1): D288-D297. DOI: https://doi.org/10.1093/nar/gkaa991
Porto, W.F., Silva, O.N. & Franco, O.L. 2012, Prediction and rational design of antimicrobial peptides. InProtein Structure. E. Faraggi (Ed.). IntechOpen, London.
Praet, S.F.E., Purdam, C.R., Welvaert, M., Vlahovich, N., Lovell, G., Burke, L.M., Gaida, J. E., Manzanero, S., Hughes, D. & Waddington, G. 2019. Oral supplementation of specific collagen peptides combined with calf-strengthening exercises enhances function and reduces pain in achilles tendinopathy patients. Nutrients, 11(1): 76. DOI: https://doi.org/10.3390/nu11010076
Qutb, A. M., Wei, F. & Dong, W. 2020. Prediction and characterization of cationic arginine-rich plant antimicrobial peptide SM-985 from teosinte (Zea mays ssp. mexicana). Frontiers in Microbiology, 11: 1353. DOI: https://doi.org/10.3389/fmicb.2020.01353
Roy, R., Tiwari, M., Donelli, G. & Tiwari, V. 2018. Strategies for combating bacterial biofilms: A focus on anti-biofilm agents and their mechanisms of action. Virulence, 9(1): 522-554. DOI: https://doi.org/10.1080/21505594.2017.1313372
Schumacher, M. A., & Zeng, W. 2016. Structures of the activator of K. pneumonia biofilm formation, MrkH, indicates PilZ domains involved in c-di-GMP and DNA binding. Proceedings of the National Academy of Sciences of the United States of America, 113(36): 10067-10072. DOI: https://doi.org/10.1073/pnas.1607503113
Sharma, A., Gupta, P., Kumar, R. & Bhardwaj, A. 2016. dPABBs: A Novel in silico approach for predicting and designing anti-biofilm peptides. Scientific Reports, 6: 21839. DOI: https://doi.org/10.1038/srep21839
Shi, G., Kang, X., Dong, F., Liu, Y., Zhu, N., Hu, Y., Xu, H., Lao, X. & Zheng, H. 2022. DRAMP 3.0: an enhanced comprehensive data repository of antimicrobial peptides. Nucleic Acids Research, 50(D1): D488-D496. DOI: https://doi.org/10.1093/nar/gkab651
Struve, C., Bojer, M., & Krogfelt, K.A. 2009. Identification of a conserved chromosomal region encoding Klebsiella pneumoniae type 1 and type 3 fimbriae and assessment of the role of fimbriae in pathogenicity. Infection and Immunity, 77(11): 5016-5024. DOI: https://doi.org/10.1128/IAI.00585-09
Swanson, J.M.J., Henchman, R.H. & McCammon, J.A. 2004. Revisiting free energy calculations: A theoretical connection to MM/PBSA and direct calculation of the association free energy. Biophysical Journal, 86(1): 67-74. DOI: https://doi.org/10.1016/S0006-3495(04)74084-9
Torrent, M., Andreu, D., Nogués, V.M. & Boix, E. 2011. Connecting peptide physicochemical and antimicrobial properties by a rational prediction model. PLoS One, 6(2): e16968. DOI: https://doi.org/10.1371/journal.pone.0016968
van Zundert, G.C.P., Rodrigues, J.P.G.L.M., Trellet, M., Schmitz, C., Kastritis, P.L., Karaca, E., Melquiond, A.S.J., van Dijk, M., de Vries, S.J. & Bonvin, A.M.J.J. 2016. The HADDOCK2.2 Web server: user-friendly integrative modeling of biomolecular complexes. Journal of Molecular Biology, 428(4): 720-725. DOI: https://doi.org/10.1016/j.jmb.2015.09.014
Vangone, A., Schaarschmidt, J., Koukos, P., Geng, C., Citro, N., Trellet, M. E., Xue, L.C. & Bonvin, A.M.J.J. 2019. Large-scale prediction of binding affinity in protein-small ligand complexes: the PRODIGY-LIG web server. Bioinformatics, 35(9): 1585-1587. DOI: https://doi.org/10.1093/bioinformatics/bty816
Waghu, F.H., Barai, R.S., Gurung, P. & Idicula-Thomas, S. 2016. CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Research, 44(D1): D1094-D1097. DOI: https://doi.org/10.1093/nar/gkv1051
Wilksch, J.J., Yang, J., Clements, A., Gabbe, J.L., Short, K.R., Cao, H., Cavaliere, R., James, C.E., Whitchurch, C.B., Schembri, M.A., Chuah, M.L.C., Liang, Z.-X., Wijburg, O.L., Jenney, A.W., Lithgow, T. & Strugnell, R.A. 2011. MrkH, a novel c-di-GMP-dependent transcriptional activator, controls Klebsiella pneumoniae biofilm formation by regulating type 3 fimbriae expression. PLoS Pathogens, 7(8): e1002204. DOI: https://doi.org/10.1371/journal.ppat.1002204
Win, T.S., Malik, A.A., Prachayasittikul, V., Wikberg, J.E.S., Nantasenamat, C. & Shoombuatong, W. 2017. HemoPred: a web server for predicting the hemolytic activity of peptides. Future Medicinal Chemistry, 9(3): 275-291. DOI: https://doi.org/10.4155/fmc-2016-0188
Yeaman, M.R. & Yount, N.Y. 2003. Mechanisms of antimicrobial peptide action and resistance. Pharmacological Reviews, 55(1): 27-55. DOI: https://doi.org/10.1124/pr.55.1.2
Zdzieblik, D., Brame, J., Oesser, S., Gollhofer, A. & König, D. 2021. The influence of specific bioactive collagen peptides on knee joint discomfort in young physically active adults: A randomized controlled trial. Nutrients, 13(2): 523. DOI: https://doi.org/10.3390/nu13020523
Zheng, J., Lin, Z., Chen, C., Chen, Z., Lin, F., Wu, Y., Yang, S., Sun, X., Yao, W., Li, D., Yu, Z., Jin, J., Qu, D. & Deng, Q. 2018. Biofilm formation in Klebsiella pneumoniae bacteremia strains was found to be associated with CC23 and the presence of wcaG. Frontiers in Cellular and Infection Microbiology, 8: 21. DOI: https://doi.org/10.3389/fcimb.2018.00021
Published
How to Cite
Issue
Section
Any reproduction of figures, tables and illustrations must obtain written permission from the Chief Editor (wicki@ukm.edu.my). No part of the journal may be reproduced without the editor’s permission
